Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations26759
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 MiB
Average record size in memory144.0 B

Variable types

Numeric9
Text6
Categorical3

Alerts

driverId is highly overall correlated with raceId and 1 other fieldsHigh correlation
laps is highly overall correlated with positionOrderHigh correlation
points is highly overall correlated with positionOrder and 1 other fieldsHigh correlation
position is highly overall correlated with positionOrder and 1 other fieldsHigh correlation
positionOrder is highly overall correlated with laps and 4 other fieldsHigh correlation
positionText is highly overall correlated with position and 1 other fieldsHigh correlation
raceId is highly overall correlated with driverId and 1 other fieldsHigh correlation
resultId is highly overall correlated with driverId and 1 other fieldsHigh correlation
statusId is highly overall correlated with points and 1 other fieldsHigh correlation
rank is highly imbalanced (50.3%) Imbalance
resultId is uniformly distributed Uniform
resultId has unique values Unique
grid has 1638 (6.1%) zeros Zeros
points has 18589 (69.5%) zeros Zeros
laps has 2532 (9.5%) zeros Zeros

Reproduction

Analysis started2025-05-20 00:16:09.893813
Analysis finished2025-05-20 00:16:35.017620
Duration25.12 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

resultId
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct26759
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13380.977
Minimum1
Maximum26764
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.2 KiB
2025-05-19T21:16:35.336175image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1338.9
Q16690.5
median13380
Q320069.5
95-th percentile25426.1
Maximum26764
Range26763
Interquartile range (IQR)13379

Descriptive statistics

Standard deviation7726.1346
Coefficient of variation (CV)0.57739688
Kurtosis-1.199847
Mean13380.977
Median Absolute Deviation (MAD)6690
Skewness0.0003467618
Sum3.5806157 × 108
Variance59693157
MonotonicityNot monotonic
2025-05-19T21:16:35.724602image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
17875 1
 
< 0.1%
17847 1
 
< 0.1%
17846 1
 
< 0.1%
17845 1
 
< 0.1%
17844 1
 
< 0.1%
17843 1
 
< 0.1%
17842 1
 
< 0.1%
17841 1
 
< 0.1%
17840 1
 
< 0.1%
Other values (26749) 26749
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
26764 1
< 0.1%
26763 1
< 0.1%
26762 1
< 0.1%
26761 1
< 0.1%
26760 1
< 0.1%
26759 1
< 0.1%
26758 1
< 0.1%
26757 1
< 0.1%
26756 1
< 0.1%
26755 1
< 0.1%

raceId
Real number (ℝ)

High correlation 

Distinct1125
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean551.68728
Minimum1
Maximum1144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.2 KiB
2025-05-19T21:16:36.114745image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile64
Q1300
median531
Q3811
95-th percentile1075
Maximum1144
Range1143
Interquartile range (IQR)511

Descriptive statistics

Standard deviation313.26504
Coefficient of variation (CV)0.56783081
Kurtosis-1.0593698
Mean551.68728
Median Absolute Deviation (MAD)254
Skewness0.12212825
Sum14762600
Variance98134.983
MonotonicityNot monotonic
2025-05-19T21:16:36.500548image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
800 55
 
0.2%
809 47
 
0.2%
360 39
 
0.1%
359 39
 
0.1%
368 39
 
0.1%
367 39
 
0.1%
366 39
 
0.1%
365 39
 
0.1%
363 39
 
0.1%
362 39
 
0.1%
Other values (1115) 26345
98.5%
ValueCountFrequency (%)
1 20
0.1%
2 20
0.1%
3 20
0.1%
4 20
0.1%
5 20
0.1%
6 20
0.1%
7 20
0.1%
8 20
0.1%
9 20
0.1%
10 20
0.1%
ValueCountFrequency (%)
1144 20
0.1%
1143 20
0.1%
1142 20
0.1%
1141 20
0.1%
1140 20
0.1%
1139 20
0.1%
1138 20
0.1%
1137 20
0.1%
1136 20
0.1%
1135 20
0.1%

driverId
Real number (ℝ)

High correlation 

Distinct861
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean278.67353
Minimum1
Maximum862
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.2 KiB
2025-05-19T21:16:36.884538image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q157
median172
Q3399.5
95-th percentile840
Maximum862
Range861
Interquartile range (IQR)342.5

Descriptive statistics

Standard deviation282.70304
Coefficient of variation (CV)1.0144596
Kurtosis-0.38996547
Mean278.67353
Median Absolute Deviation (MAD)142
Skewness1.0208177
Sum7457025
Variance79921.009
MonotonicityNot monotonic
2025-05-19T21:16:37.218919image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 404
 
1.5%
1 356
 
1.3%
8 352
 
1.3%
22 326
 
1.2%
18 309
 
1.2%
30 308
 
1.2%
20 300
 
1.1%
815 283
 
1.1%
13 271
 
1.0%
119 257
 
1.0%
Other values (851) 23593
88.2%
ValueCountFrequency (%)
1 356
1.3%
2 184
0.7%
3 206
0.8%
4 404
1.5%
5 112
 
0.4%
6 36
 
0.1%
7 27
 
0.1%
8 352
1.3%
9 99
 
0.4%
10 95
 
0.4%
ValueCountFrequency (%)
862 1
 
< 0.1%
861 9
 
< 0.1%
860 3
 
< 0.1%
859 11
 
< 0.1%
858 36
0.1%
857 46
0.2%
856 11
 
< 0.1%
855 68
0.3%
854 44
0.2%
853 22
 
0.1%

constructorId
Real number (ℝ)

Distinct211
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.180537
Minimum1
Maximum215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.2 KiB
2025-05-19T21:16:37.578821image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median25
Q363
95-th percentile205
Maximum215
Range214
Interquartile range (IQR)57

Descriptive statistics

Standard deviation61.551498
Coefficient of variation (CV)1.226601
Kurtosis0.92464474
Mean50.180537
Median Absolute Deviation (MAD)20
Skewness1.4804598
Sum1342781
Variance3788.5869
MonotonicityNot monotonic
2025-05-19T21:16:37.935757image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 2439
 
9.1%
1 1923
 
7.2%
3 1676
 
6.3%
25 881
 
3.3%
32 871
 
3.3%
15 837
 
3.1%
9 788
 
2.9%
4 787
 
2.9%
18 672
 
2.5%
34 662
 
2.5%
Other values (201) 15223
56.9%
ValueCountFrequency (%)
1 1923
7.2%
2 140
 
0.5%
3 1676
6.3%
4 787
 
2.9%
5 536
 
2.0%
6 2439
9.1%
7 280
 
1.0%
8 78
 
0.3%
9 788
 
2.9%
10 424
 
1.6%
ValueCountFrequency (%)
215 48
 
0.2%
214 180
0.7%
213 166
0.6%
211 76
 
0.3%
210 380
1.4%
209 78
 
0.3%
208 154
0.6%
207 112
 
0.4%
206 109
 
0.4%
205 76
 
0.3%

number
Text

Distinct130
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size209.2 KiB
2025-05-19T21:16:38.435452image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length3
Median length2
Mean length1.6792855
Min length1

Characters and Unicode

Total characters44936
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)0.1%

Sample

1st row22
2nd row3
3rd row7
4th row5
5th row23
ValueCountFrequency (%)
4 1019
 
3.8%
16 1005
 
3.8%
11 1001
 
3.7%
3 994
 
3.7%
6 994
 
3.7%
8 993
 
3.7%
14 982
 
3.7%
10 976
 
3.6%
20 972
 
3.6%
2 959
 
3.6%
Other values (120) 16864
63.0%
2025-05-19T21:16:39.318676image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 11013
24.5%
2 9391
20.9%
3 4455
9.9%
4 3750
 
8.3%
6 3040
 
6.8%
5 2910
 
6.5%
7 2859
 
6.4%
8 2715
 
6.0%
0 2583
 
5.7%
9 2208
 
4.9%
Other values (2) 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44936
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 11013
24.5%
2 9391
20.9%
3 4455
9.9%
4 3750
 
8.3%
6 3040
 
6.8%
5 2910
 
6.5%
7 2859
 
6.4%
8 2715
 
6.0%
0 2583
 
5.7%
9 2208
 
4.9%
Other values (2) 12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44936
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 11013
24.5%
2 9391
20.9%
3 4455
9.9%
4 3750
 
8.3%
6 3040
 
6.8%
5 2910
 
6.5%
7 2859
 
6.4%
8 2715
 
6.0%
0 2583
 
5.7%
9 2208
 
4.9%
Other values (2) 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44936
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 11013
24.5%
2 9391
20.9%
3 4455
9.9%
4 3750
 
8.3%
6 3040
 
6.8%
5 2910
 
6.5%
7 2859
 
6.4%
8 2715
 
6.0%
0 2583
 
5.7%
9 2208
 
4.9%
Other values (2) 12
 
< 0.1%

grid
Real number (ℝ)

Zeros 

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.134796
Minimum0
Maximum34
Zeros1638
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size209.2 KiB
2025-05-19T21:16:39.632133image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median11
Q317
95-th percentile23
Maximum34
Range34
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.2028596
Coefficient of variation (CV)0.64687847
Kurtosis-0.92349181
Mean11.134796
Median Absolute Deviation (MAD)6
Skewness0.19427022
Sum297956
Variance51.881187
MonotonicityNot monotonic
2025-05-19T21:16:39.951965image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 1638
 
6.1%
1 1136
 
4.2%
7 1135
 
4.2%
4 1132
 
4.2%
11 1132
 
4.2%
9 1132
 
4.2%
5 1132
 
4.2%
3 1130
 
4.2%
10 1130
 
4.2%
8 1129
 
4.2%
Other values (25) 14933
55.8%
ValueCountFrequency (%)
0 1638
6.1%
1 1136
4.2%
2 1125
4.2%
3 1130
4.2%
4 1132
4.2%
5 1132
4.2%
6 1125
4.2%
7 1135
4.2%
8 1129
4.2%
9 1132
4.2%
ValueCountFrequency (%)
34 1
 
< 0.1%
33 13
 
< 0.1%
32 17
 
0.1%
31 18
 
0.1%
30 19
 
0.1%
29 25
 
0.1%
28 30
 
0.1%
27 46
 
0.2%
26 248
0.9%
25 301
1.1%

position
Categorical

High correlation 

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size209.2 KiB
\N
10953 
3
1135 
4
1135 
2
1133 
5
1131 
Other values (29)
11272 

Length

Max length2
Median length2
Mean length1.6261445
Min length1

Characters and Unicode

Total characters43514
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row2
3rd row3
4th row4
5th row5

Common Values

ValueCountFrequency (%)
\N 10953
40.9%
3 1135
 
4.2%
4 1135
 
4.2%
2 1133
 
4.2%
5 1131
 
4.2%
1 1128
 
4.2%
6 1124
 
4.2%
7 1104
 
4.1%
8 1076
 
4.0%
9 1038
 
3.9%
Other values (24) 5802
21.7%

Length

2025-05-19T21:16:40.282994image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n 10953
40.9%
3 1135
 
4.2%
4 1135
 
4.2%
2 1133
 
4.2%
5 1131
 
4.2%
1 1128
 
4.2%
6 1124
 
4.2%
7 1104
 
4.1%
8 1076
 
4.0%
9 1038
 
3.9%
Other values (24) 5802
21.7%

Most occurring characters

ValueCountFrequency (%)
\ 10953
25.2%
N 10953
25.2%
1 7721
17.7%
2 2094
 
4.8%
3 1861
 
4.3%
4 1743
 
4.0%
5 1660
 
3.8%
6 1557
 
3.6%
7 1441
 
3.3%
8 1300
 
3.0%
Other values (2) 2231
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43514
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
\ 10953
25.2%
N 10953
25.2%
1 7721
17.7%
2 2094
 
4.8%
3 1861
 
4.3%
4 1743
 
4.0%
5 1660
 
3.8%
6 1557
 
3.6%
7 1441
 
3.3%
8 1300
 
3.0%
Other values (2) 2231
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43514
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
\ 10953
25.2%
N 10953
25.2%
1 7721
17.7%
2 2094
 
4.8%
3 1861
 
4.3%
4 1743
 
4.0%
5 1660
 
3.8%
6 1557
 
3.6%
7 1441
 
3.3%
8 1300
 
3.0%
Other values (2) 2231
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43514
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
\ 10953
25.2%
N 10953
25.2%
1 7721
17.7%
2 2094
 
4.8%
3 1861
 
4.3%
4 1743
 
4.0%
5 1660
 
3.8%
6 1557
 
3.6%
7 1441
 
3.3%
8 1300
 
3.0%
Other values (2) 2231
 
5.1%

positionText
Categorical

High correlation 

Distinct39
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size209.2 KiB
R
8897 
F
1368 
3
 
1135
4
 
1135
2
 
1133
Other values (34)
13091 

Length

Max length2
Median length1
Mean length1.216899
Min length1

Characters and Unicode

Total characters32563
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row2
3rd row3
4th row4
5th row5

Common Values

ValueCountFrequency (%)
R 8897
33.2%
F 1368
 
5.1%
3 1135
 
4.2%
4 1135
 
4.2%
2 1133
 
4.2%
5 1131
 
4.2%
1 1128
 
4.2%
6 1124
 
4.2%
7 1104
 
4.1%
8 1076
 
4.0%
Other values (29) 7528
28.1%

Length

2025-05-19T21:16:40.583067image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
r 8897
33.2%
f 1368
 
5.1%
3 1135
 
4.2%
4 1135
 
4.2%
2 1133
 
4.2%
5 1131
 
4.2%
1 1128
 
4.2%
6 1124
 
4.2%
7 1104
 
4.1%
8 1076
 
4.0%
Other values (29) 7528
28.1%

Most occurring characters

ValueCountFrequency (%)
R 8897
27.3%
1 7724
23.7%
2 2094
 
6.4%
3 1861
 
5.7%
4 1744
 
5.4%
5 1660
 
5.1%
6 1557
 
4.8%
7 1441
 
4.4%
F 1368
 
4.2%
8 1300
 
4.0%
Other values (6) 2917
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32563
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 8897
27.3%
1 7724
23.7%
2 2094
 
6.4%
3 1861
 
5.7%
4 1744
 
5.4%
5 1660
 
5.1%
6 1557
 
4.8%
7 1441
 
4.4%
F 1368
 
4.2%
8 1300
 
4.0%
Other values (6) 2917
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32563
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 8897
27.3%
1 7724
23.7%
2 2094
 
6.4%
3 1861
 
5.7%
4 1744
 
5.4%
5 1660
 
5.1%
6 1557
 
4.8%
7 1441
 
4.4%
F 1368
 
4.2%
8 1300
 
4.0%
Other values (6) 2917
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32563
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 8897
27.3%
1 7724
23.7%
2 2094
 
6.4%
3 1861
 
5.7%
4 1744
 
5.4%
5 1660
 
5.1%
6 1557
 
4.8%
7 1441
 
4.4%
F 1368
 
4.2%
8 1300
 
4.0%
Other values (6) 2917
 
9.0%

positionOrder
Real number (ℝ)

High correlation 

Distinct39
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.794051
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.2 KiB
2025-05-19T21:16:40.882699image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median12
Q318
95-th percentile26
Maximum39
Range38
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.6659506
Coefficient of variation (CV)0.59918089
Kurtosis-0.47435927
Mean12.794051
Median Absolute Deviation (MAD)6
Skewness0.39938197
Sum342356
Variance58.766799
MonotonicityNot monotonic
2025-05-19T21:16:41.512605image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
3 1135
 
4.2%
4 1135
 
4.2%
2 1134
 
4.2%
11 1133
 
4.2%
5 1132
 
4.2%
6 1132
 
4.2%
7 1132
 
4.2%
8 1132
 
4.2%
9 1131
 
4.2%
10 1130
 
4.2%
Other values (29) 15433
57.7%
ValueCountFrequency (%)
1 1128
4.2%
2 1134
4.2%
3 1135
4.2%
4 1135
4.2%
5 1132
4.2%
6 1132
4.2%
7 1132
4.2%
8 1132
4.2%
9 1131
4.2%
10 1130
4.2%
ValueCountFrequency (%)
39 13
 
< 0.1%
38 17
 
0.1%
37 17
 
0.1%
36 18
 
0.1%
35 29
 
0.1%
34 46
 
0.2%
33 65
0.2%
32 79
0.3%
31 117
0.4%
30 156
0.6%

points
Real number (ℝ)

High correlation  Zeros 

Distinct39
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9876322
Minimum0
Maximum50
Zeros18589
Zeros (%)69.5%
Negative0
Negative (%)0.0%
Memory size209.2 KiB
2025-05-19T21:16:41.858008image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile10
Maximum50
Range50
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.3512089
Coefficient of variation (CV)2.1891419
Kurtosis10.733137
Mean1.9876322
Median Absolute Deviation (MAD)0
Skewness3.0441067
Sum53187.05
Variance18.933019
MonotonicityNot monotonic
2025-05-19T21:16:42.377326image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 18589
69.5%
2 1127
 
4.2%
4 1111
 
4.2%
6 1090
 
4.1%
1 1066
 
4.0%
3 823
 
3.1%
10 613
 
2.3%
8 474
 
1.8%
9 444
 
1.7%
12 293
 
1.1%
Other values (29) 1129
 
4.2%
ValueCountFrequency (%)
0 18589
69.5%
0.5 6
 
< 0.1%
1 1066
 
4.0%
1.33 3
 
< 0.1%
1.5 17
 
0.1%
2 1127
 
4.2%
2.5 1
 
< 0.1%
3 823
 
3.1%
3.14 1
 
< 0.1%
3.5 1
 
< 0.1%
ValueCountFrequency (%)
50 1
 
< 0.1%
36 1
 
< 0.1%
30 1
 
< 0.1%
26 37
 
0.1%
25 266
1.0%
24 1
 
< 0.1%
20 1
 
< 0.1%
19 23
 
0.1%
18 280
1.0%
16 13
 
< 0.1%

laps
Real number (ℝ)

High correlation  Zeros 

Distinct172
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.301768
Minimum0
Maximum200
Zeros2532
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size209.2 KiB
2025-05-19T21:16:42.700675image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q123
median53
Q366
95-th percentile79
Maximum200
Range200
Interquartile range (IQR)43

Descriptive statistics

Standard deviation29.496557
Coefficient of variation (CV)0.63705034
Kurtosis3.6908804
Mean46.301768
Median Absolute Deviation (MAD)17
Skewness0.69643443
Sum1238989
Variance870.04687
MonotonicityNot monotonic
2025-05-19T21:16:43.078977image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2532
 
9.5%
70 1000
 
3.7%
53 937
 
3.5%
52 821
 
3.1%
56 809
 
3.0%
57 715
 
2.7%
69 688
 
2.6%
71 658
 
2.5%
55 589
 
2.2%
58 567
 
2.1%
Other values (162) 17443
65.2%
ValueCountFrequency (%)
0 2532
9.5%
1 308
 
1.2%
2 228
 
0.9%
3 199
 
0.7%
4 183
 
0.7%
5 197
 
0.7%
6 183
 
0.7%
7 167
 
0.6%
8 188
 
0.7%
9 178
 
0.7%
ValueCountFrequency (%)
200 123
0.5%
199 4
 
< 0.1%
197 5
 
< 0.1%
196 15
 
0.1%
195 4
 
< 0.1%
194 4
 
< 0.1%
193 7
 
< 0.1%
192 1
 
< 0.1%
191 8
 
< 0.1%
190 2
 
< 0.1%

time
Text

Distinct7411
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Memory size209.2 KiB
2025-05-19T21:16:43.868116image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length11
Median length2
Mean length3.6445308
Min length2

Characters and Unicode

Total characters97524
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7167 ?
Unique (%)26.8%

Sample

1st row1:34:50.616
2nd row+5.478
3rd row+8.163
4th row+17.181
5th row+18.014
ValueCountFrequency (%)
n 19079
71.3%
8:22.19 5
 
< 0.1%
46.2 4
 
< 0.1%
5.7 4
 
< 0.1%
0.7 4
 
< 0.1%
1:29.6 4
 
< 0.1%
1.1 3
 
< 0.1%
11.061 3
 
< 0.1%
14.1 3
 
< 0.1%
20.2 3
 
< 0.1%
Other values (7401) 7647
28.6%
2025-05-19T21:16:44.971435image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
\ 19079
19.6%
N 19079
19.6%
. 7679
7.9%
1 7447
 
7.6%
+ 6552
 
6.7%
2 4763
 
4.9%
3 4583
 
4.7%
: 4446
 
4.6%
4 4210
 
4.3%
5 4006
 
4.1%
Other values (5) 15680
16.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 97524
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
\ 19079
19.6%
N 19079
19.6%
. 7679
7.9%
1 7447
 
7.6%
+ 6552
 
6.7%
2 4763
 
4.9%
3 4583
 
4.7%
: 4446
 
4.6%
4 4210
 
4.3%
5 4006
 
4.1%
Other values (5) 15680
16.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 97524
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
\ 19079
19.6%
N 19079
19.6%
. 7679
7.9%
1 7447
 
7.6%
+ 6552
 
6.7%
2 4763
 
4.9%
3 4583
 
4.7%
: 4446
 
4.6%
4 4210
 
4.3%
5 4006
 
4.1%
Other values (5) 15680
16.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 97524
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
\ 19079
19.6%
N 19079
19.6%
. 7679
7.9%
1 7447
 
7.6%
+ 6552
 
6.7%
2 4763
 
4.9%
3 4583
 
4.7%
: 4446
 
4.6%
4 4210
 
4.3%
5 4006
 
4.1%
Other values (5) 15680
16.1%
Distinct7639
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Memory size209.2 KiB
2025-05-19T21:16:45.583168image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length8
Median length2
Mean length3.4447476
Min length2

Characters and Unicode

Total characters92178
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7600 ?
Unique (%)28.4%

Sample

1st row5690616
2nd row5696094
3rd row5698779
4th row5707797
5th row5708630
ValueCountFrequency (%)
n 19079
71.3%
14259460 5
 
< 0.1%
10928200 3
 
< 0.1%
11197800 2
 
< 0.1%
5152531 2
 
< 0.1%
4988777 2
 
< 0.1%
8627000 2
 
< 0.1%
14203090 2
 
< 0.1%
14356700 2
 
< 0.1%
5486983 2
 
< 0.1%
Other values (7629) 7658
28.6%
2025-05-19T21:16:46.543086image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
\ 19079
20.7%
N 19079
20.7%
5 8471
9.2%
0 6883
 
7.5%
6 6051
 
6.6%
4 5063
 
5.5%
7 4891
 
5.3%
1 4654
 
5.0%
2 4583
 
5.0%
8 4562
 
4.9%
Other values (2) 8862
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 92178
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
\ 19079
20.7%
N 19079
20.7%
5 8471
9.2%
0 6883
 
7.5%
6 6051
 
6.6%
4 5063
 
5.5%
7 4891
 
5.3%
1 4654
 
5.0%
2 4583
 
5.0%
8 4562
 
4.9%
Other values (2) 8862
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 92178
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
\ 19079
20.7%
N 19079
20.7%
5 8471
9.2%
0 6883
 
7.5%
6 6051
 
6.6%
4 5063
 
5.5%
7 4891
 
5.3%
1 4654
 
5.0%
2 4583
 
5.0%
8 4562
 
4.9%
Other values (2) 8862
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 92178
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
\ 19079
20.7%
N 19079
20.7%
5 8471
9.2%
0 6883
 
7.5%
6 6051
 
6.6%
4 5063
 
5.5%
7 4891
 
5.3%
1 4654
 
5.0%
2 4583
 
5.0%
8 4562
 
4.9%
Other values (2) 8862
9.6%
Distinct81
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size209.2 KiB
2025-05-19T21:16:47.010252image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.9855749
Min length1

Characters and Unicode

Total characters53132
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row39
2nd row41
3rd row41
4th row58
5th row43
ValueCountFrequency (%)
n 18507
69.2%
50 309
 
1.2%
52 289
 
1.1%
53 287
 
1.1%
51 275
 
1.0%
48 230
 
0.9%
44 224
 
0.8%
55 220
 
0.8%
49 219
 
0.8%
54 217
 
0.8%
Other values (71) 5982
 
22.4%
2025-05-19T21:16:47.740218image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
\ 18507
34.8%
N 18507
34.8%
5 2988
 
5.6%
4 2917
 
5.5%
3 2035
 
3.8%
6 1748
 
3.3%
2 1580
 
3.0%
1 1436
 
2.7%
7 1016
 
1.9%
0 818
 
1.5%
Other values (2) 1580
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53132
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
\ 18507
34.8%
N 18507
34.8%
5 2988
 
5.6%
4 2917
 
5.5%
3 2035
 
3.8%
6 1748
 
3.3%
2 1580
 
3.0%
1 1436
 
2.7%
7 1016
 
1.9%
0 818
 
1.5%
Other values (2) 1580
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53132
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
\ 18507
34.8%
N 18507
34.8%
5 2988
 
5.6%
4 2917
 
5.5%
3 2035
 
3.8%
6 1748
 
3.3%
2 1580
 
3.0%
1 1436
 
2.7%
7 1016
 
1.9%
0 818
 
1.5%
Other values (2) 1580
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53132
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
\ 18507
34.8%
N 18507
34.8%
5 2988
 
5.6%
4 2917
 
5.5%
3 2035
 
3.8%
6 1748
 
3.3%
2 1580
 
3.0%
1 1436
 
2.7%
7 1016
 
1.9%
0 818
 
1.5%
Other values (2) 1580
 
3.0%

rank
Categorical

Imbalance 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size209.2 KiB
\N
18249 
2
 
410
6
 
410
5
 
410
1
 
410
Other values (21)
6870 

Length

Max length2
Median length2
Mean length1.852573
Min length1

Characters and Unicode

Total characters49573
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row5
4th row7
5th row1

Common Values

ValueCountFrequency (%)
\N 18249
68.2%
2 410
 
1.5%
6 410
 
1.5%
5 410
 
1.5%
1 410
 
1.5%
3 410
 
1.5%
4 410
 
1.5%
10 409
 
1.5%
11 409
 
1.5%
7 409
 
1.5%
Other values (16) 4823
 
18.0%

Length

2025-05-19T21:16:48.103079image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n 18249
68.2%
2 410
 
1.5%
6 410
 
1.5%
5 410
 
1.5%
1 410
 
1.5%
3 410
 
1.5%
4 410
 
1.5%
10 409
 
1.5%
11 409
 
1.5%
7 409
 
1.5%
Other values (16) 4823
 
18.0%

Most occurring characters

ValueCountFrequency (%)
\ 18249
36.8%
N 18249
36.8%
1 4934
 
10.0%
2 1481
 
3.0%
0 955
 
1.9%
3 861
 
1.7%
4 846
 
1.7%
5 817
 
1.6%
6 816
 
1.6%
7 809
 
1.6%
Other values (2) 1556
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49573
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
\ 18249
36.8%
N 18249
36.8%
1 4934
 
10.0%
2 1481
 
3.0%
0 955
 
1.9%
3 861
 
1.7%
4 846
 
1.7%
5 817
 
1.6%
6 816
 
1.6%
7 809
 
1.6%
Other values (2) 1556
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49573
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
\ 18249
36.8%
N 18249
36.8%
1 4934
 
10.0%
2 1481
 
3.0%
0 955
 
1.9%
3 861
 
1.7%
4 846
 
1.7%
5 817
 
1.6%
6 816
 
1.6%
7 809
 
1.6%
Other values (2) 1556
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49573
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
\ 18249
36.8%
N 18249
36.8%
1 4934
 
10.0%
2 1481
 
3.0%
0 955
 
1.9%
3 861
 
1.7%
4 846
 
1.7%
5 817
 
1.6%
6 816
 
1.6%
7 809
 
1.6%
Other values (2) 1556
 
3.1%
Distinct7474
Distinct (%)27.9%
Missing0
Missing (%)0.0%
Memory size209.2 KiB
2025-05-19T21:16:48.719779image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length8
Median length2
Mean length3.8502934
Min length2

Characters and Unicode

Total characters103030
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6759 ?
Unique (%)25.3%

Sample

1st row1:27.452
2nd row1:27.739
3rd row1:28.090
4th row1:28.603
5th row1:27.418
ValueCountFrequency (%)
n 18507
69.2%
1:43.026 4
 
< 0.1%
1:14.117 4
 
< 0.1%
1:17.495 4
 
< 0.1%
1:18.262 4
 
< 0.1%
1:18.904 4
 
< 0.1%
1:16.066 3
 
< 0.1%
1:35.458 3
 
< 0.1%
1:37.108 3
 
< 0.1%
1:38.160 3
 
< 0.1%
Other values (7464) 8220
30.7%
2025-05-19T21:16:49.742669image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
\ 18507
18.0%
N 18507
18.0%
1 13080
12.7%
: 8252
8.0%
. 8252
8.0%
2 5518
 
5.4%
3 5450
 
5.3%
4 4681
 
4.5%
5 3759
 
3.6%
0 3523
 
3.4%
Other values (4) 13501
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 103030
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
\ 18507
18.0%
N 18507
18.0%
1 13080
12.7%
: 8252
8.0%
. 8252
8.0%
2 5518
 
5.4%
3 5450
 
5.3%
4 4681
 
4.5%
5 3759
 
3.6%
0 3523
 
3.4%
Other values (4) 13501
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 103030
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
\ 18507
18.0%
N 18507
18.0%
1 13080
12.7%
: 8252
8.0%
. 8252
8.0%
2 5518
 
5.4%
3 5450
 
5.3%
4 4681
 
4.5%
5 3759
 
3.6%
0 3523
 
3.4%
Other values (4) 13501
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 103030
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
\ 18507
18.0%
N 18507
18.0%
1 13080
12.7%
: 8252
8.0%
. 8252
8.0%
2 5518
 
5.4%
3 5450
 
5.3%
4 4681
 
4.5%
5 3759
 
3.6%
0 3523
 
3.4%
Other values (4) 13501
13.1%
Distinct7725
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Memory size209.2 KiB
2025-05-19T21:16:50.394717image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length2
Mean length3.5418364
Min length2

Characters and Unicode

Total characters94776
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7225 ?
Unique (%)27.0%

Sample

1st row218.300
2nd row217.586
3rd row216.719
4th row215.464
5th row218.385
ValueCountFrequency (%)
n 18507
69.2%
207.069 4
 
< 0.1%
194.610 3
 
< 0.1%
222.592 3
 
< 0.1%
201.527 3
 
< 0.1%
217.668 3
 
< 0.1%
200.363 3
 
< 0.1%
210.022 3
 
< 0.1%
201.478 3
 
< 0.1%
229.633 3
 
< 0.1%
Other values (7715) 8224
30.7%
2025-05-19T21:16:51.348435image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
\ 18507
19.5%
N 18507
19.5%
2 9324
9.8%
. 8252
8.7%
1 7848
8.3%
0 5231
 
5.5%
9 4751
 
5.0%
3 3987
 
4.2%
8 3962
 
4.2%
5 3737
 
3.9%
Other values (3) 10670
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 94776
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
\ 18507
19.5%
N 18507
19.5%
2 9324
9.8%
. 8252
8.7%
1 7848
8.3%
0 5231
 
5.5%
9 4751
 
5.0%
3 3987
 
4.2%
8 3962
 
4.2%
5 3737
 
3.9%
Other values (3) 10670
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 94776
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
\ 18507
19.5%
N 18507
19.5%
2 9324
9.8%
. 8252
8.7%
1 7848
8.3%
0 5231
 
5.5%
9 4751
 
5.0%
3 3987
 
4.2%
8 3962
 
4.2%
5 3737
 
3.9%
Other values (3) 10670
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 94776
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
\ 18507
19.5%
N 18507
19.5%
2 9324
9.8%
. 8252
8.7%
1 7848
8.3%
0 5231
 
5.5%
9 4751
 
5.0%
3 3987
 
4.2%
8 3962
 
4.2%
5 3737
 
3.9%
Other values (3) 10670
11.3%

statusId
Real number (ℝ)

High correlation 

Distinct137
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.224971
Minimum1
Maximum141
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.2 KiB
2025-05-19T21:16:51.703701image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median10
Q314
95-th percentile81
Maximum141
Range140
Interquartile range (IQR)13

Descriptive statistics

Standard deviation26.026104
Coefficient of variation (CV)1.510952
Kurtosis4.1957953
Mean17.224971
Median Absolute Deviation (MAD)9
Skewness2.2384412
Sum460923
Variance677.35809
MonotonicityNot monotonic
2025-05-19T21:16:52.079310image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7674
28.7%
11 4037
15.1%
5 2026
 
7.6%
12 1613
 
6.0%
3 1062
 
4.0%
81 1025
 
3.8%
4 854
 
3.2%
6 810
 
3.0%
20 795
 
3.0%
13 731
 
2.7%
Other values (127) 6132
22.9%
ValueCountFrequency (%)
1 7674
28.7%
2 147
 
0.5%
3 1062
 
4.0%
4 854
 
3.2%
5 2026
 
7.6%
6 810
 
3.0%
7 321
 
1.2%
8 214
 
0.8%
9 139
 
0.5%
10 316
 
1.2%
ValueCountFrequency (%)
141 1
 
< 0.1%
140 4
 
< 0.1%
139 3
 
< 0.1%
138 1
 
< 0.1%
137 2
 
< 0.1%
136 1
 
< 0.1%
135 1
 
< 0.1%
132 5
 
< 0.1%
131 42
0.2%
130 60
0.2%

Interactions

2025-05-19T21:16:31.282875image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:12.511839image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:14.849438image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:17.083215image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:19.550648image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:21.789689image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:24.092522image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:26.395048image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:28.654834image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:31.537709image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:12.845258image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:15.088568image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:17.312931image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-05-19T21:16:28.899925image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:31.796113image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:13.092404image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:15.336142image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:17.563760image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:20.032823image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:22.275371image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:24.594792image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:26.883769image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:29.149500image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:32.065489image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:13.311786image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:15.568896image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:17.819067image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:20.260673image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:22.510572image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:24.824734image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:27.128059image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:29.383543image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:32.306513image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:13.546334image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:15.803852image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:18.046793image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:20.484309image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:22.737122image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:25.079111image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:27.386294image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:29.629077image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:32.555939image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:13.804196image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:16.068876image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:18.297642image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:20.730366image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:23.005861image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:25.335284image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:27.644999image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:29.931224image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:32.837563image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:14.072339image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:16.320954image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:18.803242image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:21.034831image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:23.276875image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:25.605685image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:27.909738image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:30.208597image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:33.131622image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:14.291798image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:16.557625image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:19.042650image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:21.253225image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:23.517742image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:25.867421image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:28.151287image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:30.447628image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:33.399649image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:14.561996image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:16.832206image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:19.293480image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:21.514186image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:23.801681image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:26.129578image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:28.420732image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-05-19T21:16:30.729984image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-05-19T21:16:52.354262image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
constructorIddriverIdgridlapspointspositionpositionOrderpositionTextraceIdrankresultIdstatusId
constructorId1.0000.3080.173-0.105-0.2170.1490.2020.1750.3200.2220.2980.256
driverId0.3081.0000.0660.005-0.0690.1320.0630.1540.7060.2580.6690.092
grid0.1730.0661.0000.042-0.4030.2300.2230.285-0.0330.147-0.0280.175
laps-0.1050.0050.0421.0000.4190.270-0.6800.2940.0850.1520.093-0.292
points-0.217-0.069-0.4030.4191.0000.424-0.7840.4240.1440.2700.161-0.623
position0.1490.1320.2300.2700.4241.0000.6051.0000.1270.1640.1260.184
positionOrder0.2020.0630.223-0.680-0.7840.6051.0000.650-0.0650.210-0.0710.570
positionText0.1750.1540.2850.2940.4241.0000.6501.0000.1490.1660.1490.403
raceId0.3200.706-0.0330.0850.1440.127-0.0650.1491.0000.3030.969-0.077
rank0.2220.2580.1470.1520.2700.1640.2100.1660.3031.0000.2930.118
resultId0.2980.669-0.0280.0930.1610.126-0.0710.1490.9690.2931.000-0.104
statusId0.2560.0920.175-0.292-0.6230.1840.5700.403-0.0770.118-0.1041.000

Missing values

2025-05-19T21:16:33.829058image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-19T21:16:34.593131image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

resultIdraceIddriverIdconstructorIdnumbergridpositionpositionTextpositionOrderpointslapstimemillisecondsfastestLaprankfastestLapTimefastestLapSpeedstatusId
01181122111110.0581:34:50.61656906163921:27.452218.3001
121822352228.058+5.47856960944131:27.739217.5861
231833773336.058+8.16356987794151:28.090216.7191
3418445114445.058+17.18157077975871:28.603215.4641
4518512335554.058+18.01457086304311:27.418218.3851
5618638136663.057\N\N50141:29.639212.97411
67187514177772.055\N\N5481:29.534213.2245
7818861158881.053\N\N2041:27.903217.1805
89189242\NR90.047\N\N1591:28.753215.1004
910181071218\NR100.043\N\N23131:29.558213.1663
resultIdraceIddriverIdconstructorIdnumbergridpositionpositionTextpositionOrderpointslapstimemillisecondsfastestLaprankfastestLapTimefastestLapSpeedstatusId
26749267551144848323181111110.057\N\N46181:29.438212.56711
2675026756114485221522111212120.057\N\N41151:29.200213.13411
267512675711448551524151313130.057\N\N5681:27.982216.08511
2675226758114484011718131414140.057\N\N42111:28.604214.56811
2675326759114486221461171515150.057\N\N56131:29.121213.32311
2675426760114482521020141616160.057\N\N5711:25.637222.00211
2675526761114485921530121717170.055\N\N52121:28.751214.2125
2675626762114482215779\NR180.030\N\N14191:29.482212.462130
2675726763114486134320\NR190.026\N\N5171:29.411212.6315
2675826764114481591110\NR200.00\N\N\N0\N\N4